However, the design and modeling of voxel-based continuum robots is limited by the complexity of available modeling approaches. A review of model-based and model-free static and dynamic controllers for soft robotic manipulators concludes that hybrid modeling is a promising area of research [1]}. Recently, machine learning has been employed to develop differential models for closed-loop soft robotic control [2]}. Model-based methods to predict the movement of soft robots often segment the body into constituent elements which range in computational cost from finite element models, which can take hours to simulate, to piecewise constant curvature models [3]}, [4]}, [5]} which can be used for real-time control.
